{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-domain-processing-via-hybrid-denoising","title":"Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement","arxiv_id":"1812.08914","date":"2018-12-21","proceeding":null,"authors":["Jang-Hyun Kim","Jaejun Yoo","Sanghyuk Chun","Adrian Kim","Jung-Woo Ha"],"abstract":"We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise. By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.","url_abs":"https://arxiv.org/abs/1812.08914v1","url_pdf":"https://arxiv.org/pdf/1812.08914v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"multi-domain-processing-via-hybrid-denoising","repo_url":"https://github.com/Janghyun1230/SpeechEnhancement","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.08914","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}